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TBM破岩关键参数跨工程转换关系
李海波, 李旭, 王双敬, 陈祖煜, 荆留杰
PDF(3896 KB)
PDF(3896 KB)
TBM破岩关键参数跨工程转换关系
Cross Project Conversion Relationship of Key Parameters of TBM Rock Breaking
TBM信息化施工中采集了海量数据，通过数据挖掘建立机器学习模型，是实现TBM智能化的前提.然而在TBM新建工程初期，由于数据量稀少导致机器学习模型预测效果不佳；同时由于TBM设备结构和刀盘直径存在差异，基于历史工程训练得到的机器学习模型也并不适用于新建工程.为了解决这一瓶颈问题，基于单刀受力分析、经验方法和扭剪实验模型等多种换算关系推导得到了仅与刀具数量和刀盘直径有关的物理不变量，利用由不变量组成的转换方案，可以对新建工程数据进行转换；之后针对围岩分类和机器学习模型上的应用效果，比选出最佳的破岩关键参数转换方案；进而采用遗传算法，以比选得到的转换方案不变量作为初值，迭代搜索出适合当前工程的最优转换方案不变量.研究结果表明，引绰工程（新建工程）数据经过不变量的转换后输入到引松工程（历史工程）机器学习模型，其刀盘扭矩T和刀盘推力F预测结果的拟合优度R2 分别达到了0.84和0.70.本研究采用该转换方案不变量，可将不同工程的TBM施工数据归一化，将其统一到同一个框架下进行分析，实现了基于历史工程数据训练得到的机器学习模型指导新建工程施工.研究结果可为TBM机器学习模型跨工程应用提供参考.
A large amount of data have been collected in TBM information construction, and the establishment of machine learning model through data mining is the premise of realizing TBM intelligence. However, at the initial stage of TBM construction, the prediction performance of machine learning model is poor due to the lack of data; At the same time, due to the differences in TBM equipment structure and cutterhead diameter, the machine learning model based on historical projects training is not suitable for new projects. In order to solve this bottleneck problem, the physical invariants only related to the number of cutters and the diameter of the cutter head are derived from the force analysis of a single cutter, the empirical method and the torsional shear experimental model. The new projects data can be converted by using the conversion scheme composed of invariants; Then, the conversion scheme of key parameters of rock breaking with the best application performance in surrounding rock classification and machine learning model is selected; Then, the genetic algorithm is used to iteratively search the optimal conversion scheme invariant which is suitable for the current project. The research results show that the data of Yinchao project (new project) are input into the machine learning model of Yinsong project (historical project) after “invariant” conversion, and the prediction performance R2 of cutterhead torque T and cutterhead thrust F reach 0.84 and 0.70 respectively. By using this conversion scheme invariant, the TBM construction data of different projects can be normalized and analyzed under the same framework, and the machine learning model trained based on historical project data is realized to guide the construction of new projects. The research results can provide reference for the cross project application of TBM machine learning model.
TBM / 机器学习 / 破岩关键参数 / 不变量 / 遗传算法 / 岩土工程 / 工程地质
TBM / machine learning / key parameters of rock breaking / invariant / genetic algorithm / geotechnical engineering / engineering geology
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